High Energy pp Elastic Scattering in Condensate Enclosed Chiral Bag Model and TOTEM Elastic Measurements at LHC at 7 TeV

We study high energy $\small{\rm{pp}}$ and $\small{\rm{\bar {p}p}}$ elastic scattering in the TeV region based on an effective field theory model of the proton. We phenomenologically investigate the main processes underlying elastic scattering and quantitatively describe the measured elastic d$\small{\sigma}$/dt at energies 7.0 TeV (LHC $\small{\rm{pp}}$), 1.96 TeV (Tevatron $\small{\rm{\bar {p}p}}$), and 0.630 TeV (SPS $\small{\rm{\bar {p}p}}$). Finally, we give our prediction for $\small{\rm{pp}}$ elastic d$\small{\sigma}$/dt at 14 TeV that will be measured by the TOTEM Collaboration.Comment: Presented at EDS Blois 2013 (arXiv:1309.5705

Uncertainty And Evolutionary Optimization: A Novel Approach

Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.Comment: In Proceedings of the The 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014), IEEE Press, pp. 988-983, 201